{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.326599Z",
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     "shell.execute_reply": "2025-02-07T16:16:06.328777Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.326577Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.330233Z",
     "iopub.status.busy": "2025-02-07T16:16:06.330057Z",
     "iopub.status.idle": "2025-02-07T16:16:06.339253Z",
     "shell.execute_reply": "2025-02-07T16:16:06.338329Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.330214Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.340307Z",
     "iopub.status.busy": "2025-02-07T16:16:06.339973Z",
     "iopub.status.idle": "2025-02-07T16:16:06.342817Z",
     "shell.execute_reply": "2025-02-07T16:16:06.342164Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.340286Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T16:16:06.371509Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.343504Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 944805.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.373411Z",
     "iopub.status.busy": "2025-02-07T16:16:06.372871Z",
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     "shell.execute_reply": "2025-02-07T16:16:06.382034Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.373381Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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     "iopub.status.busy": "2025-02-07T16:16:06.384638Z",
     "iopub.status.idle": "2025-02-07T16:16:06.387589Z",
     "shell.execute_reply": "2025-02-07T16:16:06.386907Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.385074Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.388376Z",
     "iopub.status.busy": "2025-02-07T16:16:06.388201Z",
     "iopub.status.idle": "2025-02-07T16:16:06.395304Z",
     "shell.execute_reply": "2025-02-07T16:16:06.394615Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.388357Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
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   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.580814Z",
     "iopub.status.busy": "2025-02-07T16:16:06.580491Z",
     "iopub.status.idle": "2025-02-07T16:16:06.692466Z",
     "shell.execute_reply": "2025-02-07T16:16:06.691912Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.580793Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.693397Z",
     "iopub.status.busy": "2025-02-07T16:16:06.693149Z",
     "iopub.status.idle": "2025-02-07T16:16:06.698897Z",
     "shell.execute_reply": "2025-02-07T16:16:06.698369Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.693377Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.699939Z",
     "iopub.status.busy": "2025-02-07T16:16:06.699727Z",
     "iopub.status.idle": "2025-02-07T16:16:06.704826Z",
     "shell.execute_reply": "2025-02-07T16:16:06.704328Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.699916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1539, 0.7210, 0.1251],\n",
      "        [0.0715, 0.3116, 0.6169]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.706017Z",
     "iopub.status.busy": "2025-02-07T16:16:06.705454Z",
     "iopub.status.idle": "2025-02-07T16:16:06.956960Z",
     "shell.execute_reply": "2025-02-07T16:16:06.956356Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.705977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:06.958195Z",
     "iopub.status.busy": "2025-02-07T16:16:06.957657Z",
     "iopub.status.idle": "2025-02-07T16:16:07.208226Z",
     "shell.execute_reply": "2025-02-07T16:16:07.207668Z",
     "shell.execute_reply.started": "2025-02-07T16:16:06.958171Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.209293Z",
     "iopub.status.busy": "2025-02-07T16:16:07.208962Z",
     "iopub.status.idle": "2025-02-07T16:16:07.219603Z",
     "shell.execute_reply": "2025-02-07T16:16:07.218997Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.209271Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.220817Z",
     "iopub.status.busy": "2025-02-07T16:16:07.220321Z",
     "iopub.status.idle": "2025-02-07T16:16:07.226529Z",
     "shell.execute_reply": "2025-02-07T16:16:07.225971Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.220795Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.227541Z",
     "iopub.status.busy": "2025-02-07T16:16:07.227221Z",
     "iopub.status.idle": "2025-02-07T16:16:07.233817Z",
     "shell.execute_reply": "2025-02-07T16:16:07.233296Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.227521Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.237544Z",
     "iopub.status.busy": "2025-02-07T16:16:07.237132Z",
     "iopub.status.idle": "2025-02-07T16:16:07.242120Z",
     "shell.execute_reply": "2025-02-07T16:16:07.241600Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.237523Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.242910Z",
     "iopub.status.busy": "2025-02-07T16:16:07.242712Z",
     "iopub.status.idle": "2025-02-07T16:16:07.403216Z",
     "shell.execute_reply": "2025-02-07T16:16:07.402645Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.242890Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.404063Z",
     "iopub.status.busy": "2025-02-07T16:16:07.403845Z",
     "iopub.status.idle": "2025-02-07T16:16:07.927748Z",
     "shell.execute_reply": "2025-02-07T16:16:07.927176Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.404043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.929018Z",
     "iopub.status.busy": "2025-02-07T16:16:07.928693Z",
     "iopub.status.idle": "2025-02-07T16:16:07.949401Z",
     "shell.execute_reply": "2025-02-07T16:16:07.948618Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.928995Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.950469Z",
     "iopub.status.busy": "2025-02-07T16:16:07.950232Z",
     "iopub.status.idle": "2025-02-07T16:16:07.955912Z",
     "shell.execute_reply": "2025-02-07T16:16:07.955266Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.950447Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.956824Z",
     "iopub.status.busy": "2025-02-07T16:16:07.956627Z",
     "iopub.status.idle": "2025-02-07T16:16:07.961177Z",
     "shell.execute_reply": "2025-02-07T16:16:07.960464Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.956803Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:07.962488Z",
     "iopub.status.busy": "2025-02-07T16:16:07.961977Z",
     "iopub.status.idle": "2025-02-07T16:16:08.073581Z",
     "shell.execute_reply": "2025-02-07T16:16:08.072897Z",
     "shell.execute_reply.started": "2025-02-07T16:16:07.962456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.075082Z",
     "iopub.status.busy": "2025-02-07T16:16:08.074462Z",
     "iopub.status.idle": "2025-02-07T16:16:08.081096Z",
     "shell.execute_reply": "2025-02-07T16:16:08.080223Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.075043Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.082571Z",
     "iopub.status.busy": "2025-02-07T16:16:08.081973Z",
     "iopub.status.idle": "2025-02-07T16:16:08.089499Z",
     "shell.execute_reply": "2025-02-07T16:16:08.088723Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.082534Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"batch_size_4096.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.091022Z",
     "iopub.status.busy": "2025-02-07T16:16:08.090649Z",
     "iopub.status.idle": "2025-02-07T16:16:08.096093Z",
     "shell.execute_reply": "2025-02-07T16:16:08.095390Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.090986Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.097491Z",
     "iopub.status.busy": "2025-02-07T16:16:08.097022Z",
     "iopub.status.idle": "2025-02-07T16:16:08.102499Z",
     "shell.execute_reply": "2025-02-07T16:16:08.101789Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.097459Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.103754Z",
     "iopub.status.busy": "2025-02-07T16:16:08.103421Z",
     "iopub.status.idle": "2025-02-07T16:16:08.112832Z",
     "shell.execute_reply": "2025-02-07T16:16:08.112139Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.103720Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.114177Z",
     "iopub.status.busy": "2025-02-07T16:16:08.113670Z",
     "iopub.status.idle": "2025-02-07T16:16:08.325125Z",
     "shell.execute_reply": "2025-02-07T16:16:08.324419Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.114144Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 4096\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:08.326571Z",
     "iopub.status.busy": "2025-02-07T16:16:08.325992Z",
     "iopub.status.idle": "2025-02-07T16:16:09.304772Z",
     "shell.execute_reply": "2025-02-07T16:16:09.304115Z",
     "shell.execute_reply.started": "2025-02-07T16:16:08.326537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:16:09.306117Z",
     "iopub.status.busy": "2025-02-07T16:16:09.305606Z",
     "iopub.status.idle": "2025-02-07T16:40:31.627786Z",
     "shell.execute_reply": "2025-02-07T16:40:31.627006Z",
     "shell.execute_reply.started": "2025-02-07T16:16:09.306085Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "26499it [24:20, 18.15it/s, epoch=49, loss=2.27, val_loss=3.11]                      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 50 / global_step 26500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T16:40:31.629126Z",
     "iopub.status.busy": "2025-02-07T16:40:31.628765Z",
     "iopub.status.idle": "2025-02-07T16:40:31.782214Z",
     "shell.execute_reply": "2025-02-07T16:40:31.781689Z",
     "shell.execute_reply.started": "2025-02-07T16:40:31.629103Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/batch_size_4096.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T16:40:31.783109Z",
     "iopub.status.busy": "2025-02-07T16:40:31.782852Z",
     "iopub.status.idle": "2025-02-07T16:40:42.807298Z",
     "shell.execute_reply": "2025-02-07T16:40:42.806774Z",
     "shell.execute_reply.started": "2025-02-07T16:40:31.783088Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "10it [00:00, 91.80it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "20it [00:00, 96.25it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:09, 98.42it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.041929895092982\n",
      "testing bleu: 0.6532809278197014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T16:40:42.808253Z",
     "iopub.status.busy": "2025-02-07T16:40:42.807913Z",
     "iopub.status.idle": "2025-02-07T16:40:42.810606Z",
     "shell.execute_reply": "2025-02-07T16:40:42.810136Z",
     "shell.execute_reply.started": "2025-02-07T16:40:42.808220Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T16:40:42.811539Z",
     "iopub.status.busy": "2025-02-07T16:40:42.811367Z",
     "iopub.status.idle": "2025-02-07T16:40:43.182969Z",
     "shell.execute_reply": "2025-02-07T16:40:43.182431Z",
     "shell.execute_reply.started": "2025-02-07T16:40:42.811519Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T16:40:43.183771Z",
     "iopub.status.busy": "2025-02-07T16:40:43.183578Z",
     "iopub.status.idle": "2025-02-07T16:40:44.435731Z",
     "shell.execute_reply": "2025-02-07T16:40:44.435220Z",
     "shell.execute_reply.started": "2025-02-07T16:40:43.183751Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  8%|▊         | 10/128 [00:00<00:01, 101.28it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in a small white boat on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
